Machine translation today has been increasingly used in line with the improvement in the performance of translation engines. This machine translation analyzes syntax and semantics of a text in a predetermined natural source language by using a computer and substitutes the text for a text in another natural language. A wide range of languages has been handled in machine translation. Thereupon, a system has been proposed to achieve multilingual translation which enables translation for not only a predetermined pair of languages, but also arbitrary languages.
In multilingual translation, it is inefficient to construct independent translation engines for all language pairs since the construction requires an enormous load upon development. Thus, there is a method of translating an original text into a predetermined intermediate language temporarily, then into a desired language.
Conventionally, there are two modes for this type of multilingual translation system.
Mode 1 A pure natural language (e.g., English) is set as an intermediate language (e.g., refer to Patent Document 1).
Mode 2 Pivot—in which translation is performed using a semantic abstract data structure without depending on a language (e.g., refer to Non-Patent Documents 1 and 2).
Mode 1 achieves automatic translation between arbitrary languages (language X to language Y) by linking two translation engines. One is an X-C translation engine for translating the language X into a natural language, the intermediate language (hereinafter, referred to as language C), and the other is a C-Y translation engine for translating the language C into the language Y.
Instead of a natural language serving as an intermediate language in Mode 1, Mode 2 employs a data structure which does not depend on a specific language. Specifically, semantics and abstracts are extracted from a text in the language X to be expressed by the data structure, and a text in the language Y is constructed based on the data structure.
Japanese Patent Unexamined Publication No. 2003-58481 (P. 5); Masaru Tomita, Jaime G. Carbonell, “Another Stride Towards Knowledge-Based Machine Translation,” COLING 1986: p. 633-638; Deryle W. Lonsdale, Alexander M. Franz, John R. R. Leavitt, “Large-scale Machine Translation: An Interlingua Approach,” Seventh International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, p. 525-530, Austin, Tex.: 1994; Hideo Watanabe, Katashi Nagao, Michael C. McCord, Arendse Bernth, “Improving Natural Language Processing by Linguistic Document Annotation,” In Processings of Workshop of the 18th International Conference on Computational Linguistics, COLING 2000 all discuss translation technology.
As previously mentioned, a multilingual translation system has been conventionally proposed. In the foregoing translation system employing an intermediate language, Mode 1 which employs a natural language as the intermediate language has the following problems in data missing:                (1) Different expressions in the source (translation source) language X may be translated into one expression in the intermediate language C. In this case, when the intermediate language C is translated into the target (translation destination) language Y, the outputted language Y and the language C have the same expression. Thus, the difference between the expressions in the language X cannot be reflected in the target language Y.        
(2) In the case where there are several ways of translating a certain expression in the source language X into the intermediate language C, accurate translation in the target language Y may not be generated if the expression is determinately translated in one of the ways.
These circumstances (1) and (2) are problems caused when a language selected as an intermediate language cannot retain sufficient data required for translating into a target language.
Meanwhile, in Mode 2 which does not employ a natural language as an intermediate language, a data structure can be constructed to retain missing data in Mode 1. However, in reality, it is extremely hard to design and implement a data structure for an intermediate language to meet various language characteristics in a wide range. Accordingly, the coverage of the translation system is very small in practice.